The diversity of soil microbial communities as affected by continuous cucumber cropping and alternative rotations under protected cultivation were evaluated using community level physiological profiles (CLPP) and random amplified polymorphic DNA (RAPD) analysis. The soils were selected from six cucumber cropping systems, which cover two cropping practices (rotation and continuous cropping) and a wide spectrum for cucumber cropping history under protected cultivation. Shannon-Weaver index and multivariate analysis were performed to characterize variations in soil microbial communities. Both CLPP and RAPD techniques demonstrated that cropping systems and plastic-greenhouse cultivation could considerably affect soil microbial functional diversity and DNA sequence diversity. The open-field soil had the highest Shannon-Weaver index (3.27 for CLPP and 1.50 for RAPD), whereas the lowest value occurred in the 7-year continuous protected cultivation soil (3.27 for CLPP and 1.50 for RAPD). The results demonstrated that continuous plastic-greenhouse cultivation and management can cause the reduction in the species diversity of the biota. Higher Shannon-Weaver index and coefficients of DNA sequence similarity were found in soils under rotation than those under continuous cropping. Cluster analysis also indicated that microbial community profiles of continuous cultivation soils were different from profiles of rotation soils. The reduction in diversity of microbial communities found in continuous cultivation soils as compared with rotation soils might be due to the differences in the quantity, quality and distribution of soil organic matter.

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This paper is concerned with nonlinear modeling and analysis of the COVID-19 pandemic currently ravaging the planet. There are two objectives: to arrive at an appropriate model that captures the collected data faithfully and to use that as a basis to explore the nonlinear behavior. We use a nonlinear susceptible, exposed, infectious and removed transmission model with added behavioral and government policy dynamics. We develop a genetic algorithm technique to identify key model parameters employing COVID-19 data from South Korea. Stability, bifurcations and dynamic behavior are analyzed. Parametric analysis reveals conditions for sustained epidemic equilibria to occur. This work points to the value of nonlinear dynamic analysis in pandemic modeling and demonstrates the dramatic influence of social and government behavior on disease dynamics.